# eyeballs

> Use this tool when you need to monitor visual changes on websites or web applications, and detect discrepancies against stored baselines. It takes URLs as input, captures screenshots, and outputs visual change detections, helping to identify layout shifts, design updates, or other visual regressions. Ideal for automated testing, quality assurance, and continuous monitoring of web interfaces.

Canonical page: https://skillsregistry.net/skills/danecodes-eyeballs  
JSON: https://api.skillsregistry.net/v1/skills/danecodes-eyeballs

## Description

MCP server for visual monitoring: take screenshots of URLs and detect visual changes against stored baselines.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-08-31

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** monitoring
- **Updated:** 2026-08-31

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/w09rk6clm2)
- **Repository:** <https://github.com/danecodes/eyeballs>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "danecodes-eyeballs"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/danecodes-eyeballs` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/danecodes-eyeballs/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt